Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data
Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mul...
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doaj-32b49d4504ba4574a61dd8bcdf28d1492020-11-24T23:09:04ZengMDPI AGRemote Sensing2072-42922017-06-019655710.3390/rs9060557rs9060557Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 DataHasituya0Zhongxin Chen1Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, China. (AGRIRS)/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No. 12, Zhongguancun Nan Dajie, Haidian District, Beijing 100081, ChinaKey Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, China. (AGRIRS)/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No. 12, Zhongguancun Nan Dajie, Haidian District, Beijing 100081, ChinaUsing plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer’s, and user’s accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively.http://www.mdpi.com/2072-4292/9/6/557mapping plastic-mulched farmlandmulti-temporal Landsat-8 imageryspectral featuretextural featureindices featuresthermal feature |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hasituya Zhongxin Chen |
spellingShingle |
Hasituya Zhongxin Chen Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data Remote Sensing mapping plastic-mulched farmland multi-temporal Landsat-8 imagery spectral feature textural feature indices features thermal feature |
author_facet |
Hasituya Zhongxin Chen |
author_sort |
Hasituya |
title |
Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data |
title_short |
Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data |
title_full |
Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data |
title_fullStr |
Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data |
title_full_unstemmed |
Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data |
title_sort |
mapping plastic-mulched farmland with multi-temporal landsat-8 data |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2017-06-01 |
description |
Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer’s, and user’s accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively. |
topic |
mapping plastic-mulched farmland multi-temporal Landsat-8 imagery spectral feature textural feature indices features thermal feature |
url |
http://www.mdpi.com/2072-4292/9/6/557 |
work_keys_str_mv |
AT hasituya mappingplasticmulchedfarmlandwithmultitemporallandsat8data AT zhongxinchen mappingplasticmulchedfarmlandwithmultitemporallandsat8data |
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